Evaluating Model Performance with Cross-Validation

@genaiexp Cross-validation is a fundamental technique in machine learning used to assess the generalizability of a model. It involves partitioning the data into subsets, training the model on some subsets, and validating it on others. In GridSearchCV, cross-validation is used to evaluate the performance of different hyperparameter combinations systematically. You can specify the number of folds or use advanced techniques like stratified k-folds for imbalanced datasets. Choosing the right cross-validation strategy is crucial for reliable model evaluation. We'll delve into the best practices for cross-validation to ensure your model performs well on unseen data.