A few years ago a member of my team, who is an actuary and Kaggle master, used to delight in tormenting the rest of us when his models out-performed ours on leaderboards.
He would always insist his out-performance was due to his process of selecting lucky seeds.
We all knew full well there’s no such thing, that generalises to the private leaderboard, but many a time I caught myself trying a few different seeds to see if I can get lucky and beat his model.
There is of course nothing wrong in running a few models with different seeds and taking the average result. That’s a recognised technique called bagging which will often improve a model at the cost of implementation complexity.