mapValues is only applicable for PairRDDs, meaning RDDs of the form RDD[(A, B)]. In that case, mapValues operates on the value only (the second part of the tuple), while map operates on the entire record (tuple of key and value).
In other words, given f: B => C and rdd: RDD[(A, B)], these two are identical (almost – see comment at the bottom):
val result: RDD[(A, C)] = rdd.map { case (k, v) => (k, f(v)) }
val result: RDD[(A, C)] = rdd.mapValues(f)
The latter is simply shorter and clearer, so when you just want to transform the values and keep the keys as-is, it’s recommended to use mapValues.
On the other hand, if you want to transform the keys too (e.g. you want to apply f: (A, B) => C), you simply can’t use mapValues because it would only pass the values to your function.
The last difference concerns partitioning: if you applied any custom partitioning to your RDD (e.g. using partitionBy), using map would “forget” that paritioner (the result will revert to default partitioning) as the keys might have changed; mapValues, however, preserves any partitioner set on the RDD.