DOC Introduce dropdowns in the User Guide

### 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)](https://scikit-learn.org/stable/modules/model_evaluation.html#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](https://scikit-learn.org/stable/developers/contributing.html#documentation), notably the "Guidelines for writing the User Guide and other reStructuredText documents" dropdown.

This is the list of sub-modules to be addressed:

- [x] [1.1. Linear Models](https://scikit-learn.org/stable/modules/linear_model.html) #26623
- [x] [~~1.2. Linear and Quadratic Discriminant Analysis~~](https://scikit-learn.org/stable/modules/lda_qda.html)
- [x] [~~1.3. Kernel ridge regression~~](https://scikit-learn.org/stable/modules/kernel_ridge.html)
- [x] [1.4. Support Vector Machines](https://scikit-learn.org/stable/modules/svm.html) #26641
- [x] [1.5. Stochastic Gradient Descent](https://scikit-learn.org/stable/modules/sgd.html) #26647
- [x] [1.6. Nearest Neighbors](https://scikit-learn.org/stable/modules/neighbors.html)  #27919
- [x] [1.7. Gaussian Processes](https://scikit-learn.org/stable/modules/gaussian_process.html) #27414
- [x] [1.8. Cross decomposition](https://scikit-learn.org/stable/modules/cross_decomposition.html) #27916
- [x] [1.9. Naive Bayes](https://scikit-learn.org/stable/modules/naive_bayes.html) #26819
- [x] [1.10. Decision Trees](https://scikit-learn.org/stable/modules/tree.html) #26699
- [x] [1.11. Ensemble methods](https://scikit-learn.org/stable/modules/ensemble.html) #27915
- [x] [~~1.12. Multiclass and multioutput algorithms~~](https://scikit-learn.org/stable/modules/multiclass.html)
- [x] [1.13. Feature selection](https://scikit-learn.org/stable/modules/feature_selection.html)  #26662
- [x] [~~1.14. Semi-supervised learning~~](https://scikit-learn.org/stable/modules/semi_supervised.html)
- [x] [~~1.15. Isotonic regression~~](https://scikit-learn.org/stable/modules/isotonic.html)
- [x] [~~1.16. Probability calibration~~](https://scikit-learn.org/stable/modules/calibration.html)
- [x] [1.17. Neural network models (supervised)](https://scikit-learn.org/stable/modules/neural_networks_supervised.html) #27920
- [x] [2.1. Gaussian mixture models](https://scikit-learn.org/stable/modules/mixture.html) #26694
- [x] [2.2. Manifold learning](https://scikit-learn.org/stable/modules/manifold.html) #26720
- [x] [2.3. Clustering](https://scikit-learn.org/stable/modules/clustering.html) #26619
- [x] [~~2.4. Biclustering~~](https://scikit-learn.org/stable/modules/biclustering.html)
- [x] [2.5. Decomposing signals in components (matrix factorization problems)](https://scikit-learn.org/stable/modules/decomposition.html) #27551
- [x] [~~2.6. Covariance estimation~~](https://scikit-learn.org/stable/modules/covariance.html)
- [x] [~~2.7. Novelty and Outlier Detection~~](https://scikit-learn.org/stable/modules/outlier_detection.html)
- [x] [2.8. Density Estimation](https://scikit-learn.org/stable/modules/density.html) #26757
- [x] [~~2.9. Neural network models (unsupervised)~~](https://scikit-learn.org/stable/modules/neural_networks_unsupervised.html) #26673
- [x] [3.1. Cross-validation: evaluating estimator performance](https://scikit-learn.org/stable/modules/cross_validation.html) #27921
- [ ] [3.2. Tuning the hyper-parameters of an estimator](https://scikit-learn.org/stable/modules/grid_search.html) #27631
- [x] [3.3. Metrics and scoring: quantifying the quality of predictions](https://scikit-learn.org/stable/modules/model_evaluation.html) #28355
- [x] [~~3.4. Validation curves: plotting scores to evaluate models~~](https://scikit-learn.org/stable/modules/learning_curve.html)
- [x] [4.1. Partial Dependence and Individual Conditional Expectation plots](https://scikit-learn.org/stable/modules/partial_dependence.html) #27187
- [x] [~~4.2. Permutation feature importance~~](https://scikit-learn.org/stable/modules/permutation_importance.html)
- [x] [~~5.1. Available Plotting Utilities~~](https://scikit-learn.org/stable/visualizations.html#available-plotting-utilities)
- [x] [6.1. Pipelines and composite estimators](https://scikit-learn.org/stable/modules/compose.html) #27022
- [x] [6.2. Feature extraction](https://scikit-learn.org/stable/modules/feature_extraction.html) #26807
- [x] [6.3. Preprocessing data](https://scikit-learn.org/stable/modules/preprocessing.html) #27922
- [x] [~~6.4. Imputation of missing values~~](https://scikit-learn.org/stable/modules/impute.html)
- [x] [~~6.5. Unsupervised dimensionality reduction~~](https://scikit-learn.org/stable/modules/unsupervised_reduction.html)
- [x] [~~6.6. Random Projection~~](https://scikit-learn.org/stable/modules/random_projection.html)
- [x] [~~6.7. Kernel Approximation~~](https://scikit-learn.org/stable/modules/kernel_approximation.html)
- [x] [~~6.8. Pairwise metrics, Affinities and Kernels~~](https://scikit-learn.org/stable/modules/metrics.html)
- [x] [~~6.9. Transforming the prediction target (y)~~](https://scikit-learn.org/stable/modules/preprocessing_targets.html)
- [x] [7.1. Toy datasets](https://scikit-learn.org/stable/datasets/toy_dataset.html) #26710
- [x] [7.2. Real world datasets](https://scikit-learn.org/stable/datasets/real_world.html) #26693
- [x] [~~7.3. Generated datasets~~](https://scikit-learn.org/stable/datasets/sample_generators.html)
- [x] [~~7.4. Loading other datasets~~](https://scikit-learn.org/stable/datasets/loading_other_datasets.html)
- [x] [~~8.1. Strategies to scale computationally: bigger data~~](https://scikit-learn.org/stable/computing/scaling_strategies.html)
- [X] [~~8.2. Computational Performance~~](https://scikit-learn.org/stable/computing/computational_performance.html)
- [x] [~~8.3. Parallelism, resource management, and configuration~~](https://scikit-learn.org/stable/computing/parallelism.html)
- [x] [9.1. Python specific serialization](https://scikit-learn.org/stable/model_persistence.html#python-specific-serialization) #26881
- [x] [~~9.2. Interoperable formats~~](https://scikit-learn.org/stable/model_persistence.html#interoperable-formats)
- [x] [~~10.1. Inconsistent preprocessing~~](https://scikit-learn.org/stable/common_pitfalls.html#inconsistent-preprocessing)
- [x] [~~10.2. Data leakage~~](https://scikit-learn.org/stable/common_pitfalls.html#data-leakage)
- [x] [10.3. Controlling randomness](https://scikit-learn.org/stable/common_pitfalls.html#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!

COMMENT:
I might think of two solutions (but I have poor understanding in HTML/CSS):

- Would need to inject an `id="xxx"` in the `<details>` tag. I don't see any straightforward way to do that.
- We could use a workaround and require to add <span> tag with the `id`, i.e. `<span id="xxx">Summary</span>` that will be inject between the `<summary>` tag.

Then, in terms of HTML, we can refer to this section using `#xxx`. What I am not sure about is if the `:ref:` directive from sphinx can be used or we should use a pure HTML tag then.

Labels: Documentation, good first issue, Meta-issue