Clustered index are great for storage space and IO for common write and retrieval patterns. If your table is accessed in one way most of the time and that way needs to be fast and therefore requires an index a clustered index saves write IO (only writing the table no secondary index) disk space (no secondary index redundantly storing the indexed columns) and retrieval time (no indirection when querying the clustered index).
This is great for certain kinds of tables, for instance log tables that need to written to quickly but queried quickly (usually by time range) and grow quite large and are append only. GIS tables which can also get quite large and are by points can be packed really tight and row found quickly. Entity tables that are mostly retrieved via primary key ID during app usage, you trade a slight performance penalty when using secondary index for maximum performance when retrieving by ID which effect everything in including foreign key checks this again saves space on writing the ID twice to disk. Tables that actually are an index for something else such that the table is kept up to date with a trigger etc and usually exist for specific access pattern and performance.