diff --git a/docs/notebooks/dwi_gp_estimation.ipynb b/docs/notebooks/dwi_gp_estimation.ipynb
index b4e01e00..2cce4b3e 100644
--- a/docs/notebooks/dwi_gp_estimation.ipynb
+++ b/docs/notebooks/dwi_gp_estimation.ipynb
@@ -452,197 +452,6 @@
     "plot_dwi(shell_data[..., dwi_vol_idx], affine, gradient=np.concatenate((np.squeeze(X_test), [1000])))\n",
     "plot_dwi(dwi_sim2, affine, gradient=np.concatenate((np.squeeze(X_test), [1000])), output_file=\"sherbrooke_3shell_b1k_gp_opt_pred.svg\")"
    ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "5c77d954",
-   "metadata": {},
-   "source": [
-    "I'm not sure the cross-validation effort and the rest is worth trying right now."
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "4697b2182f5178d7",
-   "metadata": {},
-   "source": [
-    "## Cross-validation\n",
-    "\n",
-    "Use a k-fold cross-validation and a grid search to find the best parameters."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e92ebf1204c7f889",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# This is doubling-down the parameter optimization\n",
-    "# from sklearn.model_selection import GridSearchCV, KFold\n",
-    "\n",
-    "# Define the hyperparameter grid\n",
-    "# param_grid = {\n",
-    "#     \"kernel__beta_a\": np.linspace(np.pi/4, np.pi/2, 2),\n",
-    "#     \"kernel__beta_l\": np.linspace(0.1, 1 / 2.1, 2),\n",
-    "#     \"alpha\": np.linspace(1e-3, 1e-2, 2)\n",
-    "# }\n",
-    "\n",
-    "# Define k-fold cross-validation\n",
-    "# cv = KFold(n_splits=5, shuffle=True, random_state=seed)\n",
-    "\n",
-    "# # Perform grid search with cross-validation\n",
-    "# grid_search = GridSearchCV(estimator=gpr, param_grid=param_grid, cv=cv, scoring=\"neg_mean_squared_error\")\n",
-    "# grid_search.fit(X_train, sampled_dwi)\n",
-    "\n",
-    "# print(f\"Best parameters found: {grid_search.best_params_}\")\n",
-    "# print(f\"Best cross-validation score: {-grid_search.best_score_}\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "9249e76858c166e7",
-   "metadata": {},
-   "source": [
-    "Train the GP leaving out a randomly picked diffusion-encoding gradient direction and predict on it using the optimized parameters"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "83166ebc22702475",
-   "metadata": {},
-   "source": [
-    "Define the GP instances with the optimized hiperparameters"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b7e1ca2ccc899189",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "kernel = SphericalKriging(beta_a=grid_search.best_params_[\"kernel__beta_a\"], beta_l=grid_search.best_params_[\"kernel__beta_l\"])\n",
-    "gpr = EddyMotionGPR(kernel=kernel, alpha=grid_search.best_params_[\"alpha\"], disp=disp, optimizer=optimizer)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "1ebe09c2e2fef822",
-   "metadata": {},
-   "source": [
-    "Pick a random diffusion-encoding gradient direction and call fit/predict"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "ba7e22252116b374",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "idx = rng.integers(0, len(indices))\n",
-    "idx_mask = np.zeros(len(indices), dtype=bool)\n",
-    "idx_mask[idx] = True\n",
-    "\n",
-    "X_train = bvecs_shell[~idx_mask]\n",
-    "_dwi_mask = np.repeat(brain_mask[..., np.newaxis], X_train.shape[0], axis=-1)\n",
-    "y_train = shell_data[..., ~idx_mask][_dwi_mask].reshape((X_train.shape[0], -1))\n",
-    "\n",
-    "gpr_fit = gpr.fit(X_train, y_train)\n",
-    "\n",
-    "X_test = bvecs_shell[idx_mask]\n",
-    "y_pred = gpr_fit.predict(X_test)"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "dc93122f8370d2cf",
-   "metadata": {},
-   "source": [
-    "Plot the data."
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e5c8e303b4639b8c",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "x_slice = shell_data[..., idx][slice_idx[0], :, :]\n",
-    "y_slice = shell_data[..., idx][:, slice_idx[1], :]\n",
-    "z_slice = shell_data[..., idx][:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.suptitle(\"Data\")\n",
-    "plt.show()\n",
-    "\n",
-    "# Reshape the predicted data array to the image shape\n",
-    "brain_mask_idx = np.where(brain_mask)\n",
-    "_y_pred = np.zeros((shell_data.shape[:-1]), dtype=y_train.dtype)\n",
-    "_y_pred[brain_mask_idx] = y_pred.squeeze()\n",
-    "\n",
-    "x_slice = _y_pred[slice_idx[0], :, :]\n",
-    "y_slice = _y_pred[:, slice_idx[1], :]\n",
-    "z_slice = _y_pred[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     axes[i].imshow(_slice.T, cmap=\"gray\", origin=\"lower\", aspect=\"equal\")\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.suptitle(\"GP prediction\")\n",
-    "plt.show()"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "8e9c1fd2f4d2bfd8",
-   "metadata": {},
-   "source": [
-    "Compute the RMSE and plot it"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "fd54cb47f4e2fbf3",
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# Multiply the data by the brain mask to remove spurious values that were not predicted\n",
-    "rmse = np.sqrt(np.mean(np.square(shell_data[..., idx]*brain_mask - _y_pred)))\n",
-    "_rmse_element = np.sqrt(np.square(shell_data[..., idx]*brain_mask - _y_pred))\n",
-    "\n",
-    "print(f\"RMSE: {rmse}\")\n",
-    "threshold = 10\n",
-    "n_error_thr = len(_rmse_element[_rmse_element > threshold])\n",
-    "ratio = n_error_thr / np.prod(_rmse_element.shape) * 100\n",
-    "print(f\"Number of RMSE values above {threshold}: {n_error_thr} ({ratio}%)\")\n",
-    "\n",
-    "# Plot the RSME\n",
-    "x_slice = _rmse_element[slice_idx[0], :, :]\n",
-    "y_slice = _rmse_element[:, slice_idx[1], :]\n",
-    "z_slice = _rmse_element[:, :, slice_idx[2]]\n",
-    "slices = [x_slice, y_slice, z_slice]\n",
-    "\n",
-    "fig, axes = plt.subplots(1, len(slices))\n",
-    "images = []\n",
-    "for i, _slice in enumerate(slices):\n",
-    "     images.append(axes[i].imshow(_slice.T, cmap=\"viridis\", origin=\"lower\", aspect=\"equal\"))\n",
-    "     axes[i].set_axis_off()\n",
-    "\n",
-    "plt.colorbar(images[-1])\n",
-    "plt.suptitle(\"RMSE\")\n",
-    "plt.show()"
-   ]
   }
  ],
  "metadata": {