Cross-Validation

Cross-validation is a statistical method used to estimate the skill of machine learning models by partitioning the data into subsets. The model is trained on some subsets and validated on others, allowing for a more reliable assessment of its performance compared to a single train-test split. This technique helps in reducing the risk of overfitting and provides a better understanding of how the model will perform on new data.