Stop words are extremely common function words — articles, conjunctions, prepositions, pronouns, and auxiliary verbs such as the, a, an, of, to, in, is, and, but, it — that appear so often they carry little topical meaning on their own. Text-analysis tools and search engines usually filter them out before computing statistics, a step called stop-word removal.
Why remove them? In almost any English text the most frequent words are stop words. If you rank a document's words by raw count, the top of the list is dominated by "the", "of", and "and", which tell you nothing about the subject. Strip those out and the genuinely descriptive words rise to the top — which is exactly what you want for keyword density, topic detection, tag clouds, and document similarity. Removing stop words also shrinks the data a search index has to store and speeds up matching.
The important nuance is that there is no single official stop-word list — different tools and libraries ship different lists, and the right list depends on the task. A domain can even flip a word's status: "IT" is a stop-ish pronoun in general prose but a meaningful term in a technology corpus, and negation words like "not" are often kept because dropping them can invert the meaning of a sentence in sentiment analysis. Phrase search is another case where stop words matter: the query "to be or not to be" is almost all stop words, so a search engine that strips them all would fail. Stop-word filtering happens after tokenization and often alongside lemmatisation. Our word frequency counter hides stop words by default so the meaningful words surface first.