diff --git a/docs/make.jl b/docs/make.jl
index 23cb6a3..23598f4 100644
--- a/docs/make.jl
+++ b/docs/make.jl
@@ -10,26 +10,30 @@ makedocs(;
              "Quick-start guide" => "quick_start_guide.md",
              "The model type hierarchy" => "the_model_type_hierarchy.md",
              "New model type declarations" => "type_declarations.md",
-             "Supervised models" => "supervised_models.md",
-             "Summary of methods" => "summary_of_methods.md",
-             "The form of data for fitting and predicting" => "form_of_data.md",
-             "The fit method" => "the_fit_method.md",
-             "The fitted_params method" => "the_fitted_params_method.md",
-             "The predict method" => "the_predict_method.md",
-             "The predict_joint method" => "the_predict_joint_method.md",
-             "Training losses" => "training_losses.md",
-             "Feature importances" =>  "feature_importances.md",
-             "Trait declarations" => "trait_declarations.md",
-             "Iterative models and the update! method" => "iterative_models.md",
-             "Implementing a data front end" => "implementing_a_data_front_end.md",
-             "Supervised models with a transform method" =>
-                 "supervised_models_with_transform.md",
-             "Models that learn a probability distribution" => "fitting_distributions.md",
-             "Serialization" => "serialization.md",
-             "Document strings" => "document_strings.md",
+             "Supervised models" => [
+                 "Introduction" => "supervised_models.md",
+                 "Summary of methods" => "summary_of_methods.md",
+                 "The form of data for fitting and predicting" => "form_of_data.md",
+                 "The fit method" => "the_fit_method.md",
+                 "The fitted_params method" => "the_fitted_params_method.md",
+                 "The predict method" => "the_predict_method.md",
+                 "The predict_joint method" => "the_predict_joint_method.md",
+                 "Training losses" => "training_losses.md",
+                 "Feature importances" =>  "feature_importances.md",
+                 "Trait declarations" => "trait_declarations.md",
+                 "Iterative models and the update! method" => "iterative_models.md",
+                 "Implementing a data front end" => "implementing_a_data_front_end.md",
+                 "Supervised models with a transform method" =>
+                     "supervised_models_with_transform.md",
+                 "Models that learn a probability distribution" =>
+                     "fitting_distributions.md",
+             ],
              "Unsupervised models" => "unsupervised_models.md",
              "Static models" => "static_models.md",
              "Outlier detection models" => "outlier_detection_models.md",
+             "Model wrappers" => "model_wrappers.md",
+             "Serialization" => "serialization.md",
+             "Document strings" => "document_strings.md",
              "Convenience methods" => "convenience_methods.md",
              "Where to place code implementing new models" => "where_to_put_code.md",
              "How to add models to the MLJ Model Registry" => "how_to_register.md",
diff --git a/docs/src/index.md b/docs/src/index.md
index 07b319f..5512ac0 100644
--- a/docs/src/index.md
+++ b/docs/src/index.md
@@ -8,7 +8,7 @@ API defined there, as outlined in this document.
 
 !!! tip
 
-    This is a reference document, which has become rather sprawling over the evolution of the MLJ project. We recommend starting with [Quick start guide](@ref), which covers the main points relevant to most new model implementations.
+    This is a reference document, which has become rather sprawling over the evolution of the MLJ project. We recommend starting with [Quick start guide](@ref), which covers the main points relevant to most new model implementations. Most topics are only detailed for `Supervised` models, so if you are implementing another kind of model, you may still need to refer to the [Supervised models](@ref) section. 
 
 Interface code can be hosted by the package providing the core machine learning algorithm,
 or by a stand-alone "interface-only" package, using the template