The Chain Rule for Entropy provides a way to decompose the joint entropy of multiple random variables into a sum of conditional entropies, revealing how uncertainty is distributed across the variables. This rule is fundamental in information theory for understanding the dependencies between variables and is crucial for tasks like data compression and transmission efficiency.