Cross-Validation is a technique used in machine learning to assess how well a model will perform on unseen data. It involves splitting the dataset into multiple parts, or "folds." The model is trained on some of these folds and tested on the remaining ones. This process is repeated several times, ensuring that every part of the data gets a chance to be used for both training and testing. By averaging the results from these different tests, Cross-Validation provides a more reliable estimate of a model's performance. This helps in selecting the best model and tuning its parameters, ultimately leading to better predictions on new data.