Generalized Cross Validation

Generalized Cross Validation (GCV) is a statistical method used to tune models for better accuracy. It helps determine the best level of complexity—such as how many features to include or how smooth a curve should be—by systematically testing how well the model predicts new data. GCV simulates removing parts of the data, fitting the model to what's left, and then checking how well the model predicts the missing parts. This process guides us to choose the model setting that balances fitting the data well without overfitting, ensuring good performance on unseen data.