How to handle correlated variables in a dataset?
If your model has dependent variables, then it's hard to interpret the coefficients down the road.

For example, say that your model has 3 variables: profits, costs and revenue.
You trained your linear regression model to predict revenue (silly, I know).

You could get the following coefficients: $c_{cost}=-1,c_{profits}=1,c_{revenue}=0$

Someone looking at these results, could conclude that revenue is not predictive of revenue.

This is kind of a trivial example because the linear dependency is very clear (revenue=profits-costs).

But consider it a motivation for not adding dependent variables to your model.

Correlation is a measurement of pairwise linear dependency.

It's more of a question of what type are your variables.

If your variables are continuous, Dimensionality reduction algorithms like PCA should account for the correlation.