# Main take-away
## Wrap-up
In this notebook, we presented the framework used in machine-learning to
evaluate a predictive model's performance: the cross-validation.
Besides, we presented several splitting strategies that can be used in the
general cross-validation framework. These strategies should be used wisely
when encountering some specific patterns or types of data.
Finally, we show how to perform nested cross-validation to select an optimal
model and evaluate its generalization performance.
## To go further
You can refer to the following scikit-learn examples which are related to
the concepts approached in this module:
- [Comparison of cross-validation strategies](https://scikit-learn.org/stable/auto_examples/model_selection/plot_cv_indices.html#sphx-glr-auto-examples-model-selection-plot-cv-indices-py)