The document discusses various distance metrics that can be used to quantify similarity between text documents for machine learning applications. It explains challenges in modeling text data due to its high dimensionality and sparse distributions. It then summarizes distance metrics available in Scikit-Learn and SciPy that can be used, including Euclidean, Manhattan, Chebyshev, Minkowski, Mahalanobis, Cosine, Canberra, Jaccard, and Hamming distances. It provides examples applying t-SNE visualization to embed documents from three text corpora using different distance metrics to understand how the choice of distance metric impacts the resulting visualizations.