Mercer's Theorem is a fundamental result in functional analysis that provides conditions under which a continuous, symmetric, positive semi-definite kernel function can be expressed as a sum of eigenfunctions, facilitating the use of kernel methods in machine learning. This theorem is crucial for understanding the representational capacity of kernel-based algorithms, such as support vector machines, by linking them to the theory of integral equations and Hilbert spaces.