K-Fold Cross Validation is a robust method for assessing the predictive performance of a machine learning model by partitioning the dataset into 'k' subsets, or folds, and iteratively training and validating the model 'k' times, each time using a different fold as the validation set and the remaining folds as the training set. This technique helps in reducing overfitting and provides a more generalized evaluation of the model's performance by averaging the results across all folds.