K-Fold Cross-Validation is a technique used to assess the performance of a machine learning model. It involves dividing the dataset into K equal parts, or "folds." The model is trained on K-1 folds and tested on the remaining fold. This process is repeated K times, with each fold serving as the test set once. The final performance metric is obtained by averaging the results from all K iterations. This method helps ensure that the model is evaluated on different subsets of data, providing a more reliable estimate of its ability to generalize to unseen data.