The rationale can be found in the original design .
Many hash functions strive to have little correlation between the input and output values. They attempt to uniformally distribute the output values for very similar inputs. This hash function makes no such attempt. In fact, for integers, the result of the hash function is often just the input value. So similar but different input values will often result in similar but different output values. This means that it is not appropriate as a general hash function. For example, a hash table may discard bits from the hash function resulting in likely collisions, or might have poor collision resolution when hash values are clustered together. In such cases this hash function will preform poorly.
But the standard has no such requirement for the hash function, it just requires that the hashes of two different values are unlikely to collide. Containers or algorithms designed to work with the standard hash function will have to be implemented to work well when the hash function's output is correlated to its input. Since they are paying that cost a higher quality hash function would be wasteful.
For other use cases, if you do need a higher quality hash function, then neither
the standard hash function or
appropriate. There are several options available. One is to use a second hash
on the output of this hash function, such as Thomas
Wang's hash function. This this may not work as well as a hash algorithm
tailored for the input.
For strings there are several fast, high quality hash functions available (for example MurmurHash3 and Google's CityHash), although they tend to be more machine specific. These may also be appropriate for hashing a binary representation of your data - providing that all equal values have an equal representation, which is not always the case (e.g. for floating point values).