K-Fold is a technique used in machine learning to evaluate the performance of a 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 advantage of K-Fold is that it provides a more reliable estimate of a model's performance by using different subsets of data for training and testing. This helps to reduce the risk of overfitting and ensures that the model generalizes well to unseen data.