X-Cross Validation is an advanced validation technique in machine learning that enhances the traditional k-fold cross-validation approach. In X-Cross Validation, the dataset is partitioned into multiple subsets, allowing for comprehensive testing of the model's performance across different data splits. This method helps ensure that the model generalizes well to unseen data by evaluating its accuracy, precision, and other performance metrics over various subsets. X-Cross Validation is particularly useful in preventing overfitting, as it provides a more reliable estimate of the model's capabilities by considering multiple validation scenarios, leading to improved model robustness and performance.