Yao's Principle is a concept in computer science and optimization that helps in analyzing the performance of algorithms. It states that to evaluate the performance of a randomized algorithm, one can compare it to the best possible deterministic algorithm for any input. This principle allows researchers to derive bounds on the expected performance of randomized algorithms. By applying Yao's Principle, one can simplify the analysis of complex algorithms by focusing on the worst-case scenarios. This approach is particularly useful in fields like machine learning and data structures, where understanding algorithm efficiency is crucial for developing effective solutions.