Normally the cases in which you use a Regression model is when you want to predict a continuous value from a set of given independent variables.
E.g : Let the following values be of the type [independent_variable, dependent_variable] or simply $[marks,height]$ and the values be $[2,0],[3,2],[4,5],[1,1]$. You fit a line or curve through these values ($[2,0],[3,2]$ etc.) and then see the case when a value of $[10,y]$ is given or marks of $10$ are obtained, what can be the $y$ (height) value from the fitted line or curve you had modeled.
Take a look at Linear Regression for the above type.
Classification model is used in the case when you got a set of independent variables as in the previous case but the dependent value used in training is not continuous value but tells what class the value belong to.
E.g : $([2,1],fail),([3,2],fail),([4,5],pass),([1,2],fail)$. Here [2,1] belongs to class $fail$ etc. So later time when a point say [7,8] is given, you will be finding which class (pass or fail) it might probably belong to.
For example SVM's for this case create a hyperplane(a multidimensional plane) and based on where the points falls in the space, it is going to find the class with some probability.
Simply, choose Regression if the dependent value is continuous else choose Classification if the dependent value is a class.