Gradient Boosting is a powerful machine learning technique that involves combining predictions from multiple smaller models, typically decision trees, to produce an accurate and robust prediction system. It builds models sequentially, each one aiming to correct errors from the previous models in the sequence. This approach effectively reduces errors and increases model prediction accuracy. Gradient Boosting is often used in regression and classification tasks and is related to terms like bagging and boosting.