Branch and Bound is an algorithmic technique used for solving optimization problems, particularly in operations research and computer science. It systematically explores the solution space by dividing it into smaller subproblems, or "branches," and evaluating their potential to find the best solution. The method uses bounds to eliminate subproblems that cannot yield better solutions than the current best. The process involves two main steps: branching, where the problem is divided into smaller parts, and bounding, where the algorithm calculates upper or lower limits on the possible solutions. This helps in efficiently narrowing down the search, making Branch and Bound effective for problems like the Traveling Salesman Problem and Integer Programming.