The Cramér-Rao Bound is a fundamental concept in statistics that provides a lower limit on the variance of unbiased estimators. It states that for any unbiased estimator of a parameter, the variance cannot be smaller than the inverse of the Fisher information. This means that the more information we have about a parameter, the more precise our estimates can be. In practical terms, the Cramér-Rao Bound helps researchers understand the efficiency of their estimators. If an estimator reaches this bound, it is considered efficient, meaning it uses the available information optimally. This concept is crucial in fields like signal processing and machine learning.