Describe the issue linked to the documentation

Dropdowns are implemented in #26625. They can help users avoid scrolling trough large pages and can quickly get them access to the content they are interested in.

Suggest a potential alternative/fix

Use dropdowns to hide:

• low hierarchy sections such as References, Properties, etc. See for instance the subsections in 3.3.2.16 Detection error tradeoff (DET);
• in-depth mathematical details;
• narrative that is too use-case specific;
• narrative that may only interest users that want to go beyond the pragmatics of a given tool.

Additionally:

• Do not use dropdowns for the low level section Examples, as it should stay visible to all users. Make sure that the Examples section comes right after the main discussion with the least possible folded section in-between.
• Be aware that dropdowns break cross-references. If that makes sense, hide the reference along with the text mentioning it. Else, do not use dropdown.

For more information see Contributing to documentation, notably the "Guidelines for writing the User Guide and other reStructuredText documents" dropdown.

This is the list of sub-modules to be addressed:

• 1.1. Linear Models DOC Add dropdowns to module 1.1 Linear Models #26623
• 1.2. Linear and Quadratic Discriminant Analysis
• 1.3. Kernel ridge regression
• 1.4. Support Vector Machines DOC Add dropdowns to module 1.4 SVM #26641
• 1.5. Stochastic Gradient Descent DOC Add dropdowns to Module 1.5 SGD #26647
• 1.6. Nearest Neighbors DOC: Added drop down menus to 1.6 Nearest Neighbors #27919
• 1.7. Gaussian Processes DOC Add Dropdown to Module 1.7. Gaussian Processes #27414
• 1.8. Cross decomposition DOC: Added drop down menus to 1.8 Cross Decomposition #27916
• 1.9. Naive Bayes DOC: added dropdowns to module 1.9 naive bayes #26819
• 1.10. Decision Trees Doc Add dropdowns to 1.10.decision trees #26699
• 1.11. Ensemble methods DOC: Added drop down menus to 1.11 Ensemble Methods #27915
• 1.12. Multiclass and multioutput algorithms
• 1.13. Feature selection [MRG] DOC Add dropdowns to Module 1.13 Feature Selection #26662
• 1.14. Semi-supervised learning
• 1.15. Isotonic regression
• 1.16. Probability calibration
• 1.17. Neural network models (supervised) DOC: Added drop down menus to 1.17 Neural Networks (supervised) #27920
• 2.1. Gaussian mixture models DOC Adding dropdown for module 2.1 Gaussian Mixtures #26694
• 2.2. Manifold learning DOC Adding dropdown for module 2.2 Manifold Learning #26720
• 2.3. Clustering DOC Add dropdowns to Module 2.3 Clustering #26619
• 2.4. Biclustering
• 2.5. Decomposing signals in components (matrix factorization problems) DOC add dropdown menu for Section 2.5 Decomposing signals in components #27551
• 2.6. Covariance estimation
• 2.7. Novelty and Outlier Detection
• 2.8. Density Estimation DOC Add dropdowns to module 2.8. Density Estimation #26757
• 2.9. Neural network models (unsupervised) DOC Add dropdowns to module 2.9 NN Unsupervised #26673
• 3.1. Cross-validation: evaluating estimator performance DOC: Added drop down menus to 3.1 Cross Validation #27921
• 3.2. Tuning the hyper-parameters of an estimator DOC Add dropdowns to User Guide section 3.2, "Tuning the hyper-parameters of an estimator" #27631
• 3.3. Metrics and scoring: quantifying the quality of predictions DOC Add dropdowns in Module 3.3 #28355
• 3.4. Validation curves: plotting scores to evaluate models
• 4.1. Partial Dependence and Individual Conditional Expectation plots DOC: Added dropdowns to 4.1 PDPs #27187
• 4.2. Permutation feature importance
• 5.1. Available Plotting Utilities
• 6.1. Pipelines and composite estimators [MRG] DOC Add dropdown to Module 6.1 Pipelines and composite estimators #27022
• 6.2. Feature extraction DOC Added dropdowns to 6.2 feature-extraction #26807
• 6.3. Preprocessing data DOC: Added drop down menus to 6.3 Preprocessing Data #27922
• 6.4. Imputation of missing values
• 6.5. Unsupervised dimensionality reduction
• 6.6. Random Projection
• 6.7. Kernel Approximation
• 6.8. Pairwise metrics, Affinities and Kernels
• 6.9. Transforming the prediction target (y)
• 7.1. Toy datasets DOC Add dropdowns to module 7.1 Toy datasets #26710
• 7.2. Real world datasets DOC Adding Dropdown to module 7.2 Realworld Datasets #26693
• 7.3. Generated datasets
• 7.4. Loading other datasets
• 8.1. Strategies to scale computationally: bigger data
• 8.2. Computational Performance
• 8.3. Parallelism, resource management, and configuration
• 9.1. Python specific serialization DOC Add dropdowns to module 9.1 Python specific serialization #26881
• 9.2. Interoperable formats
• 10.1. Inconsistent preprocessing
• 10.2. Data leakage
• 10.3. Controlling randomness DOC adds dropdown for 10.3 Controlling Randomness #26946

Contributors willing to address this issue, please offer one of the above sub-modules per pull request. Remember also to mention on which module you are working on.

Thanks for your help!