Beam Search is an optimization technique used to find the best sequence of actions or elements in machine learning models. It maintains a set of the most promising sequences at each step, expanding them while pruning less likely candidates based on a predefined beam width. This method balances exploration of new possibilities with exploitation of known good sequences, making it effective in tasks such as sequence generation and decision-making. Beam Search improves the efficiency and accuracy of finding optimal solutions in complex problems where a brute-force approach is impractical.