Maximum Entropy (MaxEnt) is a principle used in statistical mechanics and machine learning for making predictions or inferences under uncertainty, by selecting the probability distribution that maximizes entropy subject to given constraints. It embodies the idea of being maximally non-committal to missing information, ensuring that no unwarranted assumptions are made beyond what is known.