We value models for their simplicity, for their ability to disregard secondary and tertiary details to allow comprehension and prediction of primary effects.
Then the only question is: which level of the simplicity/correctness trade-off is appropriate in given circumstances? When teaching kids or even a non-professional adults, the appropriate level is often very heavy on the "simplicity" side. Adding more complexity results in less overall understanding, so a net-negative effect in the end.
It is not always conveyed well that they are learning a simplified model, or using an analogy. That's when kids feel lied to.
Even Von Neumann said "truth is much too complicated to allow anything but approximations."
A big difference between a lie and a simplification is whether the other party knows it's a simplification. Communicating that is independent of specific simplification you use, and it's sometimes as easy as saying "is mostly like" / "sorta" instead of "is".
One thing I adore about this community is the broad acceptance of “I don’t know” and “it depends” as the starting point for answers.
My personal rule for being an expert/consultant is to be very willing to say "I don't know, but I can find that out for you", and define that as my real expertise: know how to find out things.
Technology stacks, programming languages, all of those things come and go. The skill to pick up whatever is needed is long-term better.