Dual Decomposition is an optimization technique that breaks a complex problem into smaller, more manageable subproblems, solving each independently and then coordinating the solutions to form a global solution. It leverages the duality theory to enhance computational efficiency, making it particularly valuable for large-scale convex optimization problems.