Not an expert, but I'm guessing it must be easier to do this than to improve how the machine learning generalizes from less data.
For "synthetic data", my understanding is that it's referring to use of ML to generate brand new training samples (e.g. through GANs, though there are limitations [1]).
For winning a computer vision competition it’s probably ok but I’d be very careful about using synthetic data for systems I cared about.
On the contrary, it is used to train models but it cannot adequately capture the long tail of weird events in the real world. Hence, it cannot be relied upon, as alluded to by the parent commenter. With reference to using data collected from a simulated environment vs real world ─ this subject was discussed at some length by Elon Musk and Andrej Karpathy, at the Tesla Autonomy Day event a few weeks ago.
The thing is, when it comes to weird events, historical data can't be relied on either. The next weird thing may never have happened before.
Predicting the future is hard no matter what you do. Gathering more data and learning more efficiently from what you have are both important. Training on artificial challenges can also be useful.