Rényi Divergence is a family of measures of divergence between two probability distributions, generalizing the Kullback-Leibler divergence by introducing a parameter that allows for different weighting of probabilities. It is useful in various fields such as information theory, statistics, and machine learning for tasks like hypothesis testing and anomaly detection due to its flexibility in handling distributions with different tail behaviors.