The Bellman Optimality Principle is a foundational concept in dynamic programming and reinforcement learning, stating that the optimal policy can be determined by ensuring that at each decision point, the chosen action maximizes the expected utility based on subsequent optimal decisions. This principle allows for the decomposition of complex decision problems into simpler subproblems, facilitating efficient computation of optimal strategies through recursive methods.