The Cramér-Rao Bound is a fundamental concept in statistics that provides a lower limit on the variance of unbiased estimators. It states that no unbiased estimator can have a variance smaller than the inverse of the Fisher information, which measures the amount of information that an observable random variable carries about an unknown parameter. In practical terms, the Cramér-Rao Bound helps researchers understand the efficiency of their estimators. If an estimator achieves this bound, it is considered efficient, meaning it uses the available data optimally. This concept is crucial in fields like signal processing and machine learning, where accurate parameter estimation is essential.