The graphs are just showing the progress of the model-fitting algorithm.

Basically, you give it an equation or set of equations that you think describe the data. These equations contain a number of variables (parameters) whose values are unknown. The algorithm then tries to find the set of parameters that get the equation to match the data.

From left to right, the graphs are:

1. Current model prediction (red dashed line) overlaid on the observed data.

2. Value of the model parameters during each iteration of the fitting algorithm.

3. Distribution of the residual error (error between the model prediction and the observed data) for a given parameter set.

4. Graph of the objective function value (OFV) for a search grid of parameter 2 (P2) and parameter 4 (P4). The OFV is basically a measurement of how well the model matches the data for a given set of parameter values. Often the objective function is constructed such that the better the model fits, the smaller the value of the objective function. So the algorithm tries to minimize the value of the objective function. The downward spike in the center of the graph indicates that those values for P2 and P4 result in a (local) minimum value of the objective function, i.e. those are the parameter values at which the model most closely matches the observed data.

When your residual error is zero that must mean you've gotten it right!

If any of my models ever fit this well I would immediately be suspicious.