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 gets to be in both the training and testing sets. The primary goal of cross-validation is to prevent overfitting, where a model learns the training data too well and performs poorly on 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.