A major pro for pseudo-random number generators is that they are
deterministic, that is, you can save a load the state, start from the
same seed and reproduce results, etc. At least in science (and probably
other areas) this reproducibility can be vital in a random class.

It really depends on your application though. In my use, I was
originally using normal random to produce seeds for another programs
random number generator. This ended up producing many identical results
and thus not producing an appropriate random sampling. Rather than
trying to figure out a proper way to do this with a PRNG I decided to
just use a completely random source, urandom was close enough for my needs.

I believe that is its strongest value, not having the strange artifacts
that PRNGs have. But I'm not completely sure how true that claim is :)