The list that's returned still contains mainly tactical surprises where Stockfish inaccurately evaluated the position at the end of depth 5. I think what I'm trying to say is there are some moves in a position that aren't tactically surprising (a piece sacrifice, a crazy attacking move, etc.) but positionally surprising (a long maneuver to get a piece to a certain square that I didn't think of). These positionally surprising moves aren't captured by this methodology because they don't involve large fluctuations in valuation when the depth changes.
As to your second point, an issue with how computer chess affects the modern scene is how playing the "best" move in any given position isn't representative of how humans play. Humans carry out plans and evaluate positions to the best of their ability, but the heuristics and procedure they use aren't the same as a computer's. For example, Karjakin didn't prepare for his match against Carlsen last month by playing a bunch of games against Stockfish. Rather he probably analyzed Carlsen's past games and opening choices to come up with a strategy.
I do think you can come up with a way to prepare against individually known opponents by identifying weaknesses programmatically. You can model a human's approach to playing chess as a distribution of parameters (material, king safety, pawn structure, etc.) that take in the current position and return the best move. You also have Stockfish's evaluation which returns the "best" move. With this, it's possible that you could use build a neural network that learns to play very similarly to a certain player by using their past games as a training set and comparing the chosen move to Stockfish's move. The network could learn to mimic the heuristics that the human individual uses to make decisions and playing against this new AI would be great practice for preparing against specific opponents.