Using ridge regression (mentioned in TFA) would prefer a (1/3,1/3,1/3) average of those predictors (or a better combination, depending on their respective noises).

Using lasso (also mentioned in TFA) would prefer to pick the best of the three and drop the others.

Using elastic net would be a combination of both.

Note, though, that any method other than simple regression has tuning parameters -- depending on those, you could still end with result equivalent to plain least squares.

You can, but why trash information that is present when you can leverage it with a different approach?

Like PCA? But that way you loose physical meaning of the predictors.

PCA is a special case of factor analysis, so you are representing them as observations of a latent variable (which is often a narrative people use when explaining why two x variables are correlated)