Structured Query Language (SQL) is a powerful tool for data retrieval, but its complexity often renders databases inaccessible to non-technical users. While natural language interfaces can help bridge this gap by translating user questions into SQL queries, these users may still struggle to understand or verify the generated queries. This chapter addresses the SQL-to-Text problem: the task of converting SQL queries back into natural language descriptions. An introduction of the problem and its key challenges is given and the earlier template-based approaches are presented, before diving into the neural era of SQL-to-Text. Finally, we present the benchmarks and metrics that are used to train and evaluate neural SQL-to-Text models and present key systems that have been proposed.