CatBoost is an open-source machine learning algorithm developed by Yandex. It is designed for gradient boosting on decision trees and is particularly effective for handling categorical features without the need for extensive preprocessing. This makes it user-friendly and efficient for various data types. One of the key advantages of CatBoost is its ability to reduce overfitting and improve model accuracy through techniques like ordered boosting. It also supports parallel processing, which speeds up training times. Overall, CatBoost is a powerful tool for data scientists and machine learning practitioners looking to build robust predictive models.