Jensen's Inequality is a fundamental result in convex analysis that states for any convex function, the function's value at the expectation of a random variable is less than or equal to the expectation of the function's value at that random variable. This inequality is widely used in various fields such as probability theory, statistics, and economics to analyze and bound expectations of random variables under convex transformations.