XGBoost is an open-source machine learning library designed for efficient and scalable gradient boosting. It is widely used for classification and regression tasks due to its high performance and speed. The library implements a technique called boosting, which combines multiple weak learners to create a strong predictive model. One of the key features of XGBoost is its ability to handle missing data and its support for parallel processing, making it faster than traditional boosting methods. It also includes regularization techniques to prevent overfitting, enhancing the model's generalization capabilities on unseen data.