# Introduction Cross-validation is a technique for evaluating machine learning models by training several models on different subsets of the available data and evaluating them on the complementary subset. This helps to avoid overfitting and ensures that the model is generalizable. In this tutorial, we will use scikit-learn to explore different cross-validation techniques and their behavior. ## VM Tips After the VM startup is done, click the top left corner to switch to the **Notebook** tab to access Jupyter Notebook for practice. Sometimes, you may need to wait a few seconds for Jupyter Notebook to finish loading. The validation of operations cannot be automated because of limitations in Jupyter Notebook. If you face issues during learning, feel free to ask Labby. Provide feedback after the session, and we will promptly resolve the problem for you.
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