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. This method helps ensure that the model's performance is evaluated more reliably, as it uses different subsets of data for training and testing. By averaging the results from all K iterations, we obtain a more accurate estimate of the model's effectiveness on unseen data.