The Chernoff Bound is a mathematical tool used in probability theory to provide an upper bound on the probability that the sum of random variables deviates significantly from its expected value. It is particularly useful for analyzing the performance of algorithms in computer science and for understanding the behavior of large systems in fields like statistics and machine learning. This bound is derived from the moment-generating function and offers a way to quantify how unlikely it is for the sum of independent random variables to stray far from the mean. By applying the Chernoff Bound, researchers can make more informed decisions about the reliability and efficiency of their models and algorithms.