Cross Validation is a statistical method used to assess how well a predictive model performs. It involves dividing the 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 each fold gets a chance to be used for testing. The primary goal of Cross Validation is to prevent overfitting, which occurs when a model learns the training data too well but fails to generalize to new data. By evaluating the model's performance across different subsets, it provides a more reliable estimate of its effectiveness.