Random Projection is a dimensionality reduction technique that efficiently transforms high-dimensional data into a lower-dimensional space while preserving the essential structure of the data. This method leverages the Johnson-Lindenstrauss lemma, which states that a small set of points in high-dimensional space can be embedded into a much lower-dimensional space with minimal distortion of distances between points. The sklearn.random_projection module implements this technique using either Gaussian or sparse random matrices, allowing for faster processing times and smaller model sizes. Random Projection is particularly useful in applications where computational efficiency is crucial, such as in machine learning and data analysis.