Dimensionality reduction is a crucial technique in data science and machine learning that involves transforming high-dimensional data into a lower-dimensional space while preserving essential information. This process helps to simplify datasets, making them easier to visualize and analyze. By reducing the number of features, dimensionality reduction can mitigate issues such as overfitting, where models become too complex and fail to generalize well to new data. Common methods include Principal Component Analysis (PCA) and t-Distributed Stochastic Neighbor Embedding (t-SNE), which are widely used for exploratory data analysis and improving model performance in various applications.