What is overfitting while building model? What exactly is overfitting while building models ?

Say regression as a model, overfitting means adding bunch of independent variables to explain a dependent variable. Adding even one independent variable, will increase the the fitting ability of the model. However, there are several problems/issues to doing this and some remedies to it.
Problems:

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*The addition of the new independent variable does not significantly improve the model fitting.

*The new variable has correlation with other independent variables.

*The new variable has little to no real (theoretical) connection to the dependent variable.

Remedies:

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*Check the significance of the variable. One can use Partial F-test, likelihood ratio test, $AIC$, $BIC$, etc.

*Multicollinearity can be detected and solved using variance inflation factor, principal component, etc.

*You can use step-wise regression to include or exclude the variable.

