Conditional independence is a fundamental concept in probability theory and statistics, where two events or variables are independent given the knowledge of a third event or variable. It simplifies complex probabilistic models by reducing the number of direct dependencies, allowing for more efficient computation and inference in fields like machine learning and Bayesian networks.