KL Divergence, or Kullback-Leibler Divergence, is a statistical measure that quantifies how one probability distribution differs from a second, reference probability distribution. It is often used in fields like machine learning and information theory to assess the information lost when approximating one distribution with another. The formula for KL Divergence involves summing the products of the probabilities of events in the first distribution and the logarithm of the ratio of the probabilities of the two distributions. A KL Divergence of zero indicates that the two distributions are identical, while higher values signify greater divergence.