The following line tells R to fit a linear model (hence lm()) where GPA is modeled as a function of the interaction between verbal score, math score and the squares of those terms.
model <- lm(Gpa ~ Verb*Math +I(Verb^2) + I(Math^2), data = collgpa)
Specifically, your model is GPA = -7.22 + 0.126 x Verb + 0.117 x Math - 0.00113 x Verb^2 -0.00106 x Math^2 +0.000878 x Math x Verb. Although it only uses Math and Verb as input variables, because of the ^2 terms and the interaction, your resulting linear model has 5 coefficients plus the intercept.
The I() around Verb^2 and Math^2 forces R to treat those as separate variables when fitting the model.
Is that what you're asking?