XGBoost is renowned for its efficiency, handling sparse and imbalanced data effectively, and includes regularization to prevent overfitting. It offers built-in cross-validation, tree pruning, and handles missing values internally, enhancing model performance. Its flexibility and speed make it a preferred choice for complex classification problems.