Cross-validation is a statistical method used to assess how well a predictive model performs. It involves dividing a dataset into multiple subsets, or "folds." The model is trained on some of these folds and tested on the remaining ones. This process is repeated several times, ensuring that every data point is used for both training and testing. The primary goal of cross-validation is to prevent overfitting, where a model learns the training data too well and performs poorly on new, unseen data. By evaluating the model's performance across different subsets, cross-validation provides a more reliable estimate of its effectiveness in real-world scenarios.