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# Model Selection

Model selection is an integral part of portfolio construction and therefore appears in
most examples.

Tutorials using [`WalkForward`](https://skfolio.org/generated/skfolio.model_selection.WalkForward.html.md#skfolio.model_selection.WalkForward):
: * [Custom Pre-selection Using Volumes](https://skfolio.org/auto_examples/pre_selection/plot_3_custom_pre_selection_volumes.html.md#sphx-glr-auto-examples-pre-selection-plot-3-custom-pre-selection-volumes-py)
  * [HRP vs HERC](https://skfolio.org/auto_examples/clustering/plot_3_hrp_vs_herc.html.md#sphx-glr-auto-examples-clustering-plot-3-hrp-vs-herc-py)
  * [L1 and L2 Regularization](https://skfolio.org/auto_examples/mean_risk/plot_8_regularization.html.md#sphx-glr-auto-examples-mean-risk-plot-8-regularization-py)
  * [NCO - Combinatorial Purged CV](https://skfolio.org/auto_examples/clustering/plot_5_nco_grid_search.html.md#sphx-glr-auto-examples-clustering-plot-5-nco-grid-search-py)
  * [Stacking Optimization](https://skfolio.org/auto_examples/ensemble/plot_1_stacking.html.md#sphx-glr-auto-examples-ensemble-plot-1-stacking-py)

Tutorials using [`CombinatorialPurgedCV`](https://skfolio.org/generated/skfolio.model_selection.CombinatorialPurgedCV.html.md#skfolio.model_selection.CombinatorialPurgedCV):
: * [Drop Highly Correlated Assets](https://skfolio.org/auto_examples/pre_selection/plot_1_drop_correlated.html.md#sphx-glr-auto-examples-pre-selection-plot-1-drop-correlated-py)
  * [HRP vs HERC](https://skfolio.org/auto_examples/clustering/plot_3_hrp_vs_herc.html.md#sphx-glr-auto-examples-clustering-plot-3-hrp-vs-herc-py)
  * [NCO - Combinatorial Purged CV](https://skfolio.org/auto_examples/clustering/plot_5_nco_grid_search.html.md#sphx-glr-auto-examples-clustering-plot-5-nco-grid-search-py)

Below are dedicated Model Selection tutorials.

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<div class="sphx-glr-thumbcontainer" tooltip="This tutorial introduces MultipleRandomizedCV, which is based on the &quot;Multiple Randomized Backtests&quot; methodology of Palomar in [1]_. This cross-validation strategy performs a resampling-based evaluation by repeatedly sampling distinct asset subsets (without replacement) and contiguous time windows, then applying an inner walk-forward split to each subsample, capturing both temporal and cross-sectional variability in performance.">  <div class="sphx-glr-thumbnail-title">Multiple Randomized Cross-Validation</div>
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