Generalized cross-validation (GCV) is a statistical method used to estimate the prediction error of a model, particularly useful in the context of smoothing splines and ridge regression. It provides a computationally efficient way to select the smoothing parameter by minimizing an estimate of the prediction error without the need for a separate validation set.