Jensen-Shannon Divergence is a symmetric and smoothed measure of divergence between two probability distributions, often used to quantify the similarity between them. It is based on the Kullback-Leibler divergence and provides a finite, normalized value between 0 and 1, making it useful for comparing distributions in various applications like machine learning and information theory.