XGBoost, or Extreme Gradient Boosting, is an advanced and efficient implementation of the gradient boosting framework designed to optimize model performance and training speed. It is particularly popular in machine learning competitions and real-world applications due to its scalability, flexibility, and support for parallel processing. XGBoost combines the strengths of decision trees with gradient descent optimization to minimize prediction errors iteratively. Key features include regularization techniques to prevent overfitting, handling missing values, and feature importance evaluation. Its effectiveness has made XGBoost a go-to choice for structured data problems, leading to state-of-the-art results in various domains.