K-Fold Cross-Validation is a robust statistical method used to evaluate the performance of a machine learning model by partitioning the dataset into k subsets, or 'folds', and iteratively training and testing the model k times, each time using a different fold as the test set and the remaining folds as the training set. This approach helps in minimizing overfitting and provides a more accurate estimate of the model's performance on unseen data by averaging the results from each fold.