Possible harm in standardizing one-hot encoded features
With unpenalized linear models, there is no difference.  The coefficients will just scale to counteract the new scale of the variables, and the intercept will shift to compensate for the centering.
With penalized linear models though, there will be a difference.  Since the standard deviation of a binary variable is at most $1/2$, you'll be increasing the overall scale of the variable by standardizing.  That will cause the unpenalized coefficient to decrease in magnitude, which will change the balance of how the penalty applies to different features.  I suspect there is no &quot;better&quot; approach then: sometimes the penalty improves performance when the dummies are scaled, and sometimes degrades performance.