Dimensionality Reduction is a technique used in Machine Learning to reduce the number of input variables in a dataset. This process helps to simplify models, improve performance, and reduce overfitting by eliminating redundant or irrelevant features. Common methods include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE). By transforming high-dimensional data into a lower-dimensional space, Dimensionality Reduction makes it easier to visualize and interpret complex datasets. It is particularly useful in fields like Image Processing and Natural Language Processing, where data can be vast and intricate, allowing for more efficient analysis and insights.