Temporal Difference Learning is a reinforcement learning method that updates value estimates based on the difference between predicted and actual rewards over time, allowing for learning without a model of the environment. It combines ideas from Monte Carlo methods and dynamic programming, making it efficient for learning in complex, stochastic environments.