Mutual Information quantifies the amount of information obtained about one random variable through the other, capturing the dependency between them. It is a fundamental concept in information theory used to measure the reduction in uncertainty of one variable given knowledge of another, and is crucial for applications like feature selection and clustering in machine learning.