TF-IDF, Term Frequency times Inverse Document Frequency, is a statistical formula from information retrieval that scores how important a word is to one specific document relative to a larger collection of documents. Term Frequency measures how often a word appears within a single piece of content; Inverse Document Frequency measures how rare that word is across the whole collection, so a word that's common everywhere (like "the") scores low even if it appears constantly, while a word that's distinctive to a smaller set of documents scores higher.
The practical SEO use is content-gap analysis: comparing the terms and phrases that appear across several top-ranking pages for a query against a draft or existing page reveals which relevant terms and sub-topics are underrepresented, potential signs a topic isn't being covered as thoroughly as the pages already ranking. It's a lens on topical coverage, though, not a scoring system to game directly; the goal is confirming a piece genuinely covers what searchers and ranking pages already establish as relevant, not stuffing in every term a TF-IDF tool surfaces to hit a target score.
