Reproducing Kernel Hilbert Space (RKHS) is a framework in functional analysis where functions can be evaluated by inner products with kernel functions, allowing for powerful techniques in machine learning and statistics. RKHS provides a structured way to handle infinite-dimensional spaces, enabling efficient computation and generalization in algorithms like support vector machines and Gaussian processes.