Dimensionality Reduction simplifies complex datasets by transforming them into a lower-dimensional space. This essential process in Machine Learning reveals underlying patterns, making data easier to visualize and analyze. It aims to preserve crucial information while discarding noise, often through techniques like Feature Extraction.