CatBoost is a gradient boosting library designed for categorical feature handling, developed by Yandex. It is known for its high efficiency and accuracy in machine learning tasks such as classification, regression, and ranking. CatBoost automatically deals with categorical variables without the need for extensive preprocessing, making it user-friendly. It also offers features like overfitting control, model interpretability, and multi-threading support, making it suitable for both beginners and advanced data scientists. CatBoost is widely used in competitions like Kaggle and in real-world business applications.